LEADER 05420nam 2200661 a 450 001 9910456680203321 005 20200520144314.0 010 $a1-283-24992-8 010 $a9786613249920 010 $a0-12-391887-1 035 $a(CKB)2550000000045207 035 $a(EBL)767259 035 $a(OCoLC)753480159 035 $a(SSID)ssj0000507844 035 $a(PQKBManifestationID)12214330 035 $a(PQKBTitleCode)TC0000507844 035 $a(PQKBWorkID)10549710 035 $a(PQKB)10195267 035 $a(MiAaPQ)EBC767259 035 $a(CaSebORM)9780123918864 035 $a(Au-PeEL)EBL767259 035 $a(CaPaEBR)ebr10496416 035 $a(EXLCZ)992550000000045207 100 $a20110414d2012 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aEnvironmental data analysis with MatLab$b[electronic resource] /$fWilliam Menke, Joshua Menke 205 $a1st ed. 210 $aAmsterdam ;$aBoston $cElsevier$dc2012 215 $a1 online resource (282 p.) 300 $aDescription based upon print version of record. 311 $a0-12-391886-3 320 $aIncludes bibliographical references and index. 327 $aFront Cover; Environmental Data Analysis with MatLab; Copyright; Dedication; Preface; Advice on scripting for beginners; Contents; Chapter 1: Data analysis with MatLab; 1.1. Why MatLab?; 1.2. Getting started with MatLab; 1.3. Getting organized; 1.4. Navigating folders; 1.5. Simple arithmetic and algebra; 1.6. Vectors and matrices; 1.7. Multiplication of vectors of matrices; 1.8. Element access; 1.9. To loop or not to loop; 1.10. The matrix inverse; 1.11. Loading data from a file; 1.12. Plotting data; 1.13. Saving data to a file; 1.14. Some advice on writing scripts; Problems 327 $aChapter 2: A first look at data2.1. Look at your data!; 2.2. More on MatLab graphics; 2.3. Rate information; 2.4. Scatter plots and their limitations; Problems; Chapter 3: Probability and what it has to do with data analysis; 3.1. Random variables; 3.2. Mean, median, and mode; 3.3. Variance; 3.4. Two important probability density functions; 3.5. Functions of a random variable; 3.6. Joint probabilities; 3.7. Bayesian inference; 3.8. Joint probability density functions; 3.9. Covariance; 3.10. Multivariate distributions; 3.11. The multivariate Normal distributions 327 $a3.12. Linear functions of multivariate dataProblems; Chapter 4: The power of linear models; 4.1. Quantitative models, data, and model parameters; 4.2. The simplest of quantitative models; 4.3. Curve fitting; 4.4. Mixtures; 4.5. Weighted averages; 4.6. Examining error; 4.7. Least squares; 4.8. Examples; 4.9. Covariance and the behavior of error; Problems; Chapter 5: Quantifying preconceptions; 5.1. When least square fails; 5.2. Prior information; 5.3. Bayesian inference; 5.4. The product of Normal probability density distributions; 5.5. Generalized least squares 327 $a5.6. The role of the covariance of the data5.7. Smoothness as prior information; 5.8. Sparse matrices; 5.9. Reorganizing grids of model parameters; Problems; Chapter 6: Detecting periodicities; 6.1. Describing sinusoidal oscillations; 6.2. Models composed only of sinusoidal functions; 6.3. Going complex; 6.4. Lessons learned from the integral transform; 6.5. Normal curve; 6.6. Spikes; 6.7. Area under a function; 6.8. Time-delayed function; 6.9. Derivative of a function; 6.10. Integral of a function; 6.11. Convolution; 6.12. Nontransient signals; Problems 327 $aChapter 7: The past influences the present7.1. Behavior sensitive to past conditions; 7.2. Filtering as convolution; 7.3. Solving problems with filters; 7.4. Predicting the future; 7.5. A parallel between filters and polynomials; 7.6. Filter cascades and inverse filters; 7.7. Making use of what you know; Problems; Chapter 8: Patterns suggested by data; 8.1. Samples as mixtures; 8.2. Determining the minimum number of factors; 8.3. Application to the Atlantic Rocks dataset; 8.4. Spiky factors; 8.5. Time-Variable functions; Problems; Chapter 9: Detecting correlations among data 327 $a9.1. Correlation is covariance 330 $a Environmental Data Analysis with MatLab is for students and researchers working to analyze real data sets in the environmental sciences. One only has to consider the global warming debate to realize how critically important it is to be able to derive clear conclusions from often-noisy data drawn from a broad range of sources. This book teaches the basics of the underlying theory of data analysis, and then reinforces that knowledge with carefully chosen, realistic scenarios. MatLab, a commercial data processing environment, is used in these scenarios; significant content is devoted to teachi 606 $aEnvironmental sciences$xMathematical models 606 $aEnvironmental sciences$xData processing 608 $aElectronic books. 615 0$aEnvironmental sciences$xMathematical models. 615 0$aEnvironmental sciences$xData processing. 676 $a363.7001/5118 700 $aMenke$b William$067453 701 $aMenke$b Joshua E$g(Joshua Ephraim),$f1976-$0961070 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910456680203321 996 $aEnvironmental data analysis with MatLab$92178959 997 $aUNINA